R: Latent Dirichlet Allocation

spark.lda {SparkR}

R Documentation

Latent Dirichlet Allocation

Description

spark.lda fits a Latent Dirichlet Allocation model on a SparkDataFrame. Users can call summary to get a summary of the fitted LDA model, spark.posterior to compute posterior probabilities on new data, spark.perplexity to compute log perplexity on new data and write.ml/read.ml to save/load fitted models.

Usage

spark.lda(data, ...)

spark.posterior(object, newData)

spark.perplexity(object, data)

## S4 method for signature 'SparkDataFrame'
spark.lda(
  data,
  features = "features",
  k = 10,
  maxIter = 20,
  optimizer = c("online", "em"),
  subsamplingRate = 0.05,
  topicConcentration = -1,
  docConcentration = -1,
  customizedStopWords = "",
  maxVocabSize = bitwShiftL(1, 18)
)

## S4 method for signature 'LDAModel'
summary(object, maxTermsPerTopic)

## S4 method for signature 'LDAModel,SparkDataFrame'
spark.perplexity(object, data)

## S4 method for signature 'LDAModel,SparkDataFrame'
spark.posterior(object, newData)

## S4 method for signature 'LDAModel,character'
write.ml(object, path, overwrite = FALSE)

Arguments

`data`	A SparkDataFrame for training.
`...`	additional argument(s) passed to the method.
`object`	A Latent Dirichlet Allocation model fitted by `spark.lda`.
`newData`	A SparkDataFrame for testing.
`features`	Features column name. Either libSVM-format column or character-format column is valid.
`k`	Number of topics.
`maxIter`	Maximum iterations.
`optimizer`	Optimizer to train an LDA model, "online" or "em", default is "online".
`subsamplingRate`	(For online optimizer) Fraction of the corpus to be sampled and used in each iteration of mini-batch gradient descent, in range (0, 1].
`topicConcentration`	concentration parameter (commonly named `beta` or `eta`) for the prior placed on topic distributions over terms, default -1 to set automatically on the Spark side. Use `summary` to retrieve the effective topicConcentration. Only 1-size numeric is accepted.
`docConcentration`	concentration parameter (commonly named `alpha`) for the prior placed on documents distributions over topics (`theta`), default -1 to set automatically on the Spark side. Use `summary` to retrieve the effective docConcentration. Only 1-size or `k`-size numeric is accepted.
`customizedStopWords`	stopwords that need to be removed from the given corpus. Ignore the parameter if libSVM-format column is used as the features column.
`maxVocabSize`	maximum vocabulary size, default 1 << 18
`maxTermsPerTopic`	Maximum number of terms to collect for each topic. Default value of 10.
`path`	The directory where the model is saved.
`overwrite`	Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

Value

spark.lda returns a fitted Latent Dirichlet Allocation model.

summary returns summary information of the fitted model, which is a list. The list includes

`docConcentration`	concentration parameter commonly named `alpha` for the prior placed on documents distributions over topics `theta`
`topicConcentration`	concentration parameter commonly named `beta` or `eta` for the prior placed on topic distributions over terms
`logLikelihood`	log likelihood of the entire corpus
`logPerplexity`	log perplexity
`isDistributed`	TRUE for distributed model while FALSE for local model
`vocabSize`	number of terms in the corpus
`topics`	top 10 terms and their weights of all topics
`vocabulary`	whole terms of the training corpus, NULL if libsvm format file used as training set
`trainingLogLikelihood`	Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs \| topics, topic distributions for docs, Dirichlet hyperparameters) It is only for distributed LDA model (i.e., optimizer = "em")
`logPrior`	Log probability of the current parameter estimate: log P(topics, topic distributions for docs \| Dirichlet hyperparameters) It is only for distributed LDA model (i.e., optimizer = "em")

spark.perplexity returns the log perplexity of given SparkDataFrame, or the log perplexity of the training data if missing argument "data".

spark.posterior returns a SparkDataFrame containing posterior probabilities vectors named "topicDistribution".

Note

spark.lda since 2.1.0

summary(LDAModel) since 2.1.0

spark.perplexity(LDAModel) since 2.1.0

spark.posterior(LDAModel) since 2.1.0

write.ml(LDAModel, character) since 2.1.0

Examples

## Not run: 
##D text <- read.df("data/mllib/sample_lda_libsvm_data.txt", source = "libsvm")
##D model <- spark.lda(data = text, optimizer = "em")
##D 
##D # get a summary of the model
##D summary(model)
##D 
##D # compute posterior probabilities
##D posterior <- spark.posterior(model, text)
##D showDF(posterior)
##D 
##D # compute perplexity
##D perplexity <- spark.perplexity(model, text)
##D 
##D # save and load the model
##D path <- "path/to/model"
##D write.ml(model, path)
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)

[Package SparkR version 3.0.1 Index]